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Application of covering algorithm for classification of melanoma spots on the skin

100%

Grzymała-Busse J. W. , Hippe Z. S.

2002

tom Vol. 10, nr 2

27-33

An in-house elaborated and implemented covering algorithm was applied. For the development of learning models (in the form of production rules), used then for computer-assisted classification (hence, diagnosing) of melanoma spots on the skin. In our research, four types of marks (namely, Benign nevus, Blue nevus, Suspicious nevus, and Melanoma malignant) have be en investigated. One of the generated learning models (the most promimissing one) was optimized by execution of selected generic operations on production rules, what lead to a very concise set of rules (4 rules only) giving errorless classification of unseen cases tested.

Approximation Space and LEM2-like Algorithms for Computing Local Coverings

100%

Grzymała-Busse J. W. , Rząsa W.

2008

tom Vol. 85, nr 1-4

205-217

In this paper we discuss approximation spaces that are useful for studying local lower and upper approximations. Set definability and properties of the approximation space, including best approximations, are considered as well. Finding best approximations is a NP-hard problem. Finally, we present LEM2-like algorithms for determining local lower and upper coverings for a given incomplete data set. Lower and upper approximations, associated with these coverings, are sub-optimal.

A new algorithm for generation of decision trees

80%

Grzymała-Busse J. W. , Hippe Z. S. , Knap M. , Mroczek T.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2004

tom Vol. 8, No 2

243-247

A new algorithm for development of quasi-optimal decision trees, based on the Bayes theorem, has been created and tested. The algorithm generates a decision tree on the basis of Bayesian belief networks, created prior to the formation of the decision tree. The efficiency of this new algorithm was compared with three other known algorithms used to develop decision trees. The data set used for the experiments was a set of cases of skin lesions, histopatolgically verified.

Diagnosing skin melanoma: current versus future directions

51%

Hippe Z. S. , Bajcar S. , Blajdo P. , Grzymała-Busse J. P. , Grzymała-Busse J. W. , Knap M. , Paja W. , Wrzesień M.

tom Vol. 7, No 2

289-293

A new database containing 410 cases of nevi pigmentosi, in four categories: benign nevus, blue nevus, suspicious nevus and melanoma malignant, carefully verified by histopathology, is described. The database is entirely different from the base presented previously, and can be readily used for research based on the so-called constructive induction in machine learning. To achieve this, the database features a different set of thirteen descriptive attributes, with a fourteenth additional attribute computed by applying values of the remaining thirteen attributes. In addition, a new program environment for the validation of computer-assisted diagnosis of melanoma, is briefly discussed. Finally, results are presented on determining optimal coefficients for the well-known ABCD formula, useful for melanoma diagnosis.